Sports analytics and data science in sports are topics that are getting a lot of traction lately in the sports world. Every year, more and more teams are being united around intelligence, and in this edition of the UpTech Report, we meet with the CEO of Kitman Labs, Stephen Smith, to learn why.
Kitman Labs is a company focused on improving human performance in sports by analyzing athlete data. Their Intelligence Platform allows you to consolidate millions of data points about game stats, market value, scouting scores, training volumes, speed, strength, injuries, sleep, mental state, basically everything about an sports player. Even biometrics and blood markers are on the table.
However, it’s not solely about the data either. At Kitman Labs, the real focus is on turning that data into intelligence and empowering the right people on the team to make the right decisions at the right times.
Stephen founded Kitman Labs in 2012 with the vision of revolutionizing health and performance in elite sport. Stephen’s passion for blending sports medicine, science, leading user experience techniques and machine learning has resulted in industry-first innovations that bring meaning to the masses of data collected on athletes, quantify injury risk, and simplify daily life for coaches and staff.
These innovations are used by more than 700 teams across the globe in more than 45 different leagues including some of the best-known sporting brands. His ten-year background as Senior Injury Rehabilitation and Conditioning Coach with the Leinster Rugby Club in Ireland and his Master’s thesis on combined risk factors as predictors of athletic injury served as the foundation of Kitman Labs.show more
Stephen is a self-proclaimed sports science and athletic performance nerd and has been described as one of the leading minds on injury reduction, health data analytics, and performance enhancement in elite sport. He speaks all over the world discussing research, advanced analytical techniques, and practical application of sports science and technology.show less
DISCLAIMER: Below is an AI generated transcript. There could be a few typos but it should be at least 90% accurate. Watch video or listen to the podcast for the full experience!
Stephen Smith 0:00
We actually became the first northern hemisphere team of any sport to use GPS technology on athletes use wearable sensors and athletes and
Alexander Ferguson 0:12
welcome to UpTech Report. This is our apply tech series UpTech Report is sponsored by TeraLeap. Learn how to leverage the power of video at teraleap.io. Today, I’m excited to be joined by my guest, Stephe Smith, who’s based in Palo Alto. He’s the CEO at Kitman labs. Welcome, Stephen. Good to have you on.
Stephen Smith 0:30
Great to be here. Thank you so much, Alexander.
Alexander Ferguson 0:33
Now, Kitman Labs is an analytics platform. And you’re focused on generating insights to help professional athletes perform better. Help me understand. So you’re like, what was the problem that you saw and set out to solve?
Stephen Smith 0:45
Yeah, the problem was big, right? I think even I come from a background working in sports medicine and strength conditioning, and spent my entire career working in professional sport prior to this company. And I think when I started in sport, there was very little data being collected. And, you know, I think the the electronic medical records product of choice at that point in time, and in my club was a whiteboard. So the injuries would get written on a whiteboard. And, you know, when they would get fixed, they would wipe them away. And that was, that was where they went. And, you know, the, how we tracked what was happening in training was, you know, stop watching a whistle, and we write down how many minutes we were on the field for and, you know, so it was sports come a long way over the last 15 to 20 years. And when, when we first started on on kind of our journey with the club that I was involved in, and when a new head coach came in, and he asked a lot of questions about, you know, what types of injuries are occurring? You know, when did they occur? How do they occur? What position groups pick them off? You know, how long do they last for? He also had a lot of questions about what does a physical profile of a professional athlete look like? You know, how much better do they get? We bring them in? How does that vary by position group? And, you know, where do we make them strong? Where do make them weak? What does the fitness profile look like? You know, and what does that look like for somebody who has a five year career versus somebody who has a 1015 year career? So he had all of these questions, and we had very little answer. It’s very little data and very little information. So I think we, you know, first the mandate was, can we digitize the athlete lifecycle, started to gather and document what was happening on a day to day basis. So it was this, this was back in 2007 2007 2008. And in at that point in time, we actually became the first northern hemisphere team of any sport to use GPS technology on athletes use wearable sensors and athletes and, and that was a huge step forward, because we went from having an understanding of the fact that, you know, a practice session lasts for 40 minutes, or 50 minutes, or whatever it was, to now having 300 data points on our athletes 60 times a second across all of our athletes. So, you know, we were drinking from a firehose, pretty, pretty fast. And, you know, we knew what distances they were traveling, we knew many times, we were stepping off the left foot off the right foot, we knew the number of accelerations decelerations the number of collisions. And I think one of the one of the first things that occurred to us was like a sense of being overwhelmed, too, right? We had all this data. And really, we ended up kind of looking at some pretty simplistic metrics, like the total distance somebody ran or, you know, the amount of kind of high speed running that was happening, like very simplistic things, and just looking at, you know, how that would change over time. And what we, you know, we knew we weren’t really leveraging it correctly. And we started also performing strength profiling, we started collecting biomechanical data, Bloods and hormonal profiling, Nam psychological data, recovery, testing data. So you name it, we went from basic nothing to like collecting 64 streams of independent data. Every day, and I think that left us in a position where we realized that there was there was just so much, there was probably too much. And also, what was happening was it was starting to become weaponized. So different practitioners would use the data to tell different stories that supported their mandate within the environment. So you know, if a fitness coach solid or strangulation coats on, oftentimes we would, we would look at the athletes who hadn’t done as much, you know, distance and having, you know, covered as much accelerations or decelerations and just didn’t get the same physical stimulus from a session. And we would look at that and think, Okay, well, maybe we need to do more with them. And the sports medicine side, we’ll look at the opposite and see, okay, well, this guy has done more than everybody else, you know, maybe they need to do less. And the reality was, we didn’t actually know whether one should be doing more, one should be doing less and we weren’t factoring in all the other things about those athletes. And, and, you know, I think we were just we were just looking at stories and building narratives, and we wanted to do better so I said I performing my master’s thesis on the whole concept of combining risk factors as predictors of the athletic injury. So we took the date that diagnostic code for a medical record and started to build pattern identification around that with the what we report the stress and strain and load that we’re placing on our athletes, and then how they were recovering and responding. And then we started to use that the modeling for decision making.
We had a pretty significant reduction in injuries just over 30% year on year for two years, subsequently went on to win our first European Cup, but then when three more European trophies was that was there, and that was kind of the start. That was the start of and I’m not saying that our technology and what we were doing that was the secret sauce, right. And I’m a realist, right? I think we had incredible talent, really incredible athletes. We had incredible coaches, we great game strategy, we just superb culture, you know, we had all the right ingredients, but we were also making an impact. And we were keeping our best talent on the field. And that was helping us basically to capitalize on the opportunity that, you know, collectively as a whole we were building. And so that’s kind of where it all started.
Alexander Ferguson 6:01
There. There’s a lot to unpack there. And in the journey is obviously the the insights that started the whole piece when it comes to data science in sports for me personally, and I imagine maybe for some others, that the movie Moneyball with Brad Pitt, that was maybe the first introduction, oh, wow, you can use all this, all these data points and whatever coming into it. Is that similar to this, or is it just completely different?
Stephen Smith 6:28
Yeah, I think it was a similar approach in that, you know, what Billy Beane, and you know, Paul depodesta, and that crew were doing at the at the A’s was, they were trying to, you know, take digitize what was the, you know, baseball athletes doing on the field, they were trying to basically, you know, pin down objective numbers that led them to understand what performance look like, they were using it to try and find value in the in the market and make the right trades and get the right, the right people that could deliver the right performance. We started from a different angle. But ultimately, it’s exactly the same concept, right? Can we kind of instead of relying on our gut instinct and experience, which are really important? Can we support that instinct with data? Like, can we support that with objectivity? And can we inform the decisions? And can we guide our intuition and support our intuition? And with numerical data to validate or invalidate what we’re seeing what we’re hearing what we’re feeling what we’re thinking? So I think in many ways, the approaches are very synergistic.
Alexander Ferguson 7:28
Those in in sports management, those who are leading the different teams, are they did they quickly recognize and jump on this data? And say, yes, we need more data, we need more analytics on what our what our what our team members are doing?
Stephen Smith 7:41
Yeah, I think at the start, what we saw, when we first started doing this, we saw teams were like, yes, the data revolution is here. Let’s jump in. Let’s get knee deep. Let’s do this. And I think that very quickly got to the point where people were like, well, now I have it all. What What am I actually going to do with it? So yeah, across the world of sport today, people feel data rich and insights poor. And that’s one of the one of the things that we’re most excited about within this industry is, is helping people and helping people and helping this industry to mature, right. It’s not just about data collection, it’s not just about building a data landscape, that data is useless and the investment that you’re making is useless, unless we can you know, really start to cultivate that and really do something powerful with it and turn it into turn it into impact.
Alexander Ferguson 8:28
Starting in Dublin, Ireland as this rehabilitation coach, is that right? Injury Rehabilitation,
Stephen Smith 8:35
rehab was my specialty. So I have like an athletic training background, and then a strength conditioning certification as well. So I came from both sides and ended up like working on injury prevention, rehabilitation as my specialty
Alexander Ferguson 8:46
and you start kipping labs. While you’re still there. Is that right? 2012 I think I see a number here as but you’re still there till 2014. So it was like, while you were there, you begin this?
Stephen Smith 8:58
Yeah, exactly. So we actually founded the company kicked it off, while I was still working full time in sport, because I didn’t start this company and think, Okay, we are, you know, we’re going to go and build this, you know, huge global company and raise all this money and have hundreds of people working for us. And, you know, all these things that have happened I started it because I was passionate about solving a problem and I’m still am today and and I actually had approached the club to look for funding to build what we wanted to build internally, and they couldn’t afford it at that point in time. So we you know, I ended up going and raising some capital to do it because it was just so passionate about it. And but I also loved what I did every day like I’m a practitioner at heart and I love being on the ground with athletes and I love like being in that environment to something quite special about it. And I think that’s also that’s a it’s a really important part of the culture of our organization is that we are at our heart a group of elite practitioners who have you know, worked at the top level and sport and believe that this industry deserves better And I think that that’s one of the things that makes us very different from anybody that we’ve ever seen in this industry. And it’s why I think we’re approaching this problem from a very different angle. Because we’re not a group of business people or just a group of people. We’re not just a group of technologists, you know, our heart and soul. We’re practitioners.
Alexander Ferguson 10:18
You start with rugby, does it does the concept of what you’re applying translate to every sport?
Stephen Smith 10:24
Yeah, I mean, what we’re what we’re doing is essentially understanding human performance, right? At its soul, like, that’s what we’re doing is understanding human performance and human failure. And, you know, I suppose we started in rugby, because that’s where it came from, very quickly, we bled into football, or soccer, as we call it over here. And, and it started to scale from there. And now I think, I don’t know we work across 25 plus different sports. And, and the reality is, even if you look at it, like at the start, we a lot of people poking at that saying, Oh, you’re just a really big company, or just soccer company. And, you know, performance is different to everybody. Even within a rugby environment, you don’t like you know, if you think about this, from an NFL perspective, if you walked into the Patriots locker room, or you walked into the New York Giants locker room, their version of performance, their definition of performance is different. So you know how Bill Belichick wants his team to play the strategy that they’re going to deploy how they’re going to pick apart the opponent, that’s going to be completely different than say, Urban Meyer after Jags right there. All of these like coaches their their definition of the perfect game, and what that looks like, that is different. And therefore what our role is to come in and support that. And not do that in an off the shelf way, right? There is no one size fits all at this level of elite sport. So our role is to come in and understand what is that perfect visual performance? How do they want them to play? You know, how fast do they need to be to do that? How powerful they do they need to be to do that? How do they need to practice and train like in the build up to that to be able to exhibit that version of performance that they have in the perfect? You know, how resilient do they need to be to be able to carry out that game plan and that strategy to that effect against different types of opposition, etc. So you know, when you start to understand it like that, and look at it like that every team is unique. It’s not really about the sport, of course, there are uniqueness that we need to understand. There’s context, it’s incredibly specific to each sport. But really what we’re doing is like, we’re building this unique system for how we basically help people to understand you know, how they optimize performance, how they develop performance, how do I they identify the right talent for that particular type of performance, and how they keep their athletes resilient to be able to play in and display that performance?
Alexander Ferguson 12:37
If I didn’t say Go get your Givens let your your lab, your athlete management system has 150 data integrations, is that right?
Stephen Smith 12:46
Yeah, so are you know, as we call our system, athlete management system is not necessarily the phrase that we would use for what we do. In our mind. You know, what we’ve seen in the industry, athlete management systems are basically just data platforms, they’re first generation legacy databases, just consume information. And they, you know, produce data insights on top of that, or reports or visualizations, on top of that data. So they’re smart databases configured for sport. And we build an intelligence platform. And that’s a really important distinction, because we believe in everything here is about intelligence. It’s not about bringing your data together. It’s not about putting in a database. It’s not about visualizing, it’s not about technology, right? The technology for us is a tool, right? What it’s all about is intelligence, how do we get the right information to the right people at the right time to empower the right decisions, and that there’s a there’s a very distinct difference between data and intelligence. And for us, you know, our that that is a really, really important distinction, because everything that we do is geared towards helping people to leverage that. And obviously, you know, 150 Plus, I don’t know, it’s far more than that, at this point integrations, if we can’t cultivate all that data, if we can’t bring that data together, if we can’t bring all of the information in, we can’t actually generate robust insights, that that are intelligent, right? Where we’re just building a very narrow myopic view of a specific piece of data. So the key to unlocking this industry and unlocking intelligence is, is by having a holistic, comprehensive data set that you know, that’s living and breathing and growing every day.
Alexander Ferguson 14:20
You put the emphasis on intelligence, can you give me a use case or an example of in play this data, not just Okay, look at this pretty dashboard. But how is the team actually using us?
Stephen Smith 14:32
Yeah, from our perspective, like, you know, that Intel, like that whole intelligence piece of what that means for athletes is like it means better management, right? Plain and simple. Like imagine you’re an athlete, right? And you walk into a training facility feeling sore and tired. What is the coaches do with that piece of information? What do the medics do? Like what does a strength coach do? Do you train Do you play? Are you sent home the rest? Does your practice time change? The reality is right. There’s a ton of context needed. To answer that question, they need to know how you normally sleep, they need to know how sorry you normally are. They need to know what happened to affect your sleep or your soreness. They, they need to know what is the practice plan for day? And what are you needed for? And when are you next playing? And when did you last play, and what’s your current performance level up, and then realize these decisions are happening 10s of times a day, and decisions are being made in seconds. And we would like the role that we play as we provide teams with the ability to make those decisions educated. And that is like changing the way teams are fundamentally operated. We’re helping them to turn that data into intelligence. So we’re helping to bring all of those pieces together, we’re allowing all of those different practitioners, to medics, the strength conditioning coaches, the technical and tactical coaches, we’re allowing them all to develop this shared understanding of data. And then we’re building research tools that allow them to understand what does that mean. So in that, that small, that small, little unique one piece of information to prove this person is feeling sore and tired today? What does that matter? Or how do we know from others? We know if it matters by actually asking the question of what’s happened in previous scenarios like this before? What does that actually resulting? Can they actually make? Can they get the achieved or desired like training stimulus from that? Are they going to break because of that? And if you think of the amount of data sources that are needed to holistically answer that question, and the number of people that need to be informed, because the coach needs to decide what they’re going to do the strength conditioning coaches decide what to do with the medical needs to decide what they’re going to do. And they also that the ramification of what what that person do those impacts all of the other athletes as well and their plan, they need to be able to make a very coordinated decision about what’s happening and what happens for everybody else like that. How do you do that when you have 5060 different streams of data, and you have it for every athlete, and you have to make decisions for it’s not just one athlete showed up. Now, actually, there’s 10, athletes of 10 different things going on on this particular day, and in this moment, and you’re seeing them for a number of minutes before your practice day starts. So bringing that together, turning that into discernible information, and providing insights that empower those decisions. And second thought, that’s what we do
Alexander Ferguson 17:07
is simply as as the medic, or the the strength coach, just open up the dashboard, and they can type in what they’re looking for what’s happening, and then gives them insights. Are they looking at analytics? What does that experience?
Stephen Smith 17:22
Yeah, so I suppose it’s different within every environment, right. And also, it’s worth noting that, like, it’s not that you just don’t try to like open your laptop, boom, it’s there, right? You know, on on day one, right? There’s some work that goes on to bring all that data together, there’s work that goes on to basically analyze that data and understand what it means. And then basically, we built we built our visualizations and workflow tools internally within the product to allow them to, to then utilize that day to day. So when somebody does wake up, they walk in, they have their laptop, there might be specific metrics that they’re looking at, or specific insights that they’re looking at, that they see, oh, this person is outside of where we would like that person to be at, they see that they can click in, they see exactly what’s going on, they can have a discussion about that as a collaborative, high performance team, and then they’re able to go and make the decision off the back of that. So a lot of these organizations and just like if you’re working in, I don’t know, like a, you know, an engineering, you know, an engineering company or a software development company. In the morning time, your staff might come together and go like, Okay, here’s our action plan for today. Here’s the pieces that we’re working on. This is what I’m doing. This is what I’m doing. And it’s exactly the same thing. And high performance environment. People generally have those status meetings in the morning, say, Okay, where’s everybody at? What’s next? What what what to our group look like? You know, who’s feeling you know, who’s getting tired, who’s feeling sore? How people pulled up after yesterday? How’s that injury tracking, so these status briefings happen and we’re basically providing the insights that lead them to guide those make them really efficient, major decisions, and then move forward and execute on their plan.
Alexander Ferguson 18:53
Those that are looking for future careers in sports management careers of managing teams in different places and ways is it just going to be the norm now that you’re just going to always be able to have these a dashboard and be able to go in and be able to drill down to find the exact answers. And will it be just that that is the future?
Stephen Smith 19:14
100% I mean, it has to be right. That’s the bet we’re making. And that’s the role that we’re playing in the market. Like, you know, if you were if you were working in Wall Street 20 years ago, right, it was done very differently than than it is today. Right? You know, that, how they looked at stocks and trades, how it was like, how what’s happening now all that’s happening right in front of an interface, if you look at the insurance industry, and how like policies are priced, if you look at the airline industry, if you look at how we how we like how we book a taxi today, we used to stand at the street with our hand up right today. Now we’re like, if you look at how we rent movies, like we basically sit there with a remote control in front of us or our phones or tablets, whereas we used to like walk down to the video store and pick it off the shelf. All of this is changing and this is a natural evolution. And the reality is as well. We don’t want people who have spend 567 years in university studying a specific trade like sports medicine, and then going and spending 15 years building a craft, we don’t want them spending hours at a laptop every day, we want them deploying their skills, working with athletes, treating athletes, you know, working on preventative medicine, working on rehabilitative medicine, having conversations with coaches and pushing that organization forward, the last place that we want them is sitting in front of a laptop. And and that’s, you know, that’s our role is to come in and automate and provide those, like those insights, provide that intelligence and empower people to to utilize their skills in the best way they can.
Alexander Ferguson 20:37
You you’re trying to remove the technology, that it’s just, it’s just they’re allowing just allowing you to do your job better. When it comes to all these stats and these wearables that are wearing in this data that’s coming in, is it? Are we there yet? Where it’s just automated? Like it just gets in there? Or is it a lot of manual entry that needs to happen? So
Stephen Smith 20:54
yeah, for the vast majority of it today, it’s pretty automated, and, you know, most products within this space, so they have, like API’s and information just flows backwards and forwards, right? So like any, like any other space, and with every passing day, that’s getting better and better. Right? So, you know, I think there’s, there’s more automation, there’s more fidelity to data, there’s more granularity to it, there’s more consistency, there’s less errors. And so I you know, I don’t worry about data quality data consistency, within this industry any longer, you know, I think, you know, the biggest the biggest place that people need to focus their attention to is intelligence,
Alexander Ferguson 21:31
this, this conversate conversation of being able to understand and how data, this data affects this person so that we can make an educated decision, is it? Is it across all people like are you able to take the learnings from all the players everywhere that’s in the system and and be able to apply that learning and give access to that? Or is it really only relevant per individual, like only that person’s data really matters? Yeah, it’s
Stephen Smith 22:00
a great question. A third, the reality is that both right, I think there is power in the larger aggregated data set and the learnings that we can make across larger cohorts of people. But you also have to understand that every organization has different different data sources, different data quality, different data consistency. So the insights that you can dry off larger pools of data are not always totally ubiquitous. So you can make like, you become a good insights from that. But in terms of how does that apply, what we really care about is understanding individual response and under understanding the individual. So if we can take, like large scale learnings from a bigger pool, and then if we can go and refine that, then for each individual, that’s what we really care about. That’s what perfect looks like. And that’s that’s the approach that we take today.
Alexander Ferguson 22:52
The question is, are we getting to the question of just trying to how do we build the best athlete? Is that the question? Yes. Yeah.
Stephen Smith 22:59
I think it’s like, what is the perfect? Like, what does perfect performance look like? How do you like I don’t know if they’re, like, rebuilding the best athletes, because performance is different, because different coaches want different things, like, depending on the way you play, you may need to be faster, you may need to be more powerful, you may need to be more reactive, you may need to be stronger, you may need to have more endurance, like all of these variables, like there’s, you know, there’s trade offs, like the, you know, you know, a marathon runner doesn’t look like a sprinter for a reason, right. So all of like, the these decisions that we make about the way we want to play the game, you know, have like rabid like physiological and neuromuscular ramifications. So what we care about is basically helping people to develop that perfect performance. But that very, like whenever that definition of perfect performances, it’s generally different within different environments. And that’s, I think that’s, again, something that is quite unique to our organization. I think everybody, like a lot of other organizations, we’ve seen what they want to get a one size fits all approach, you want to say, this is how you do it in football. And so you’re doing basketball, let’s say you do in baseball. And the reality is, I think, you know, we, we as an industry deserve more, and our role within the industry is to give people more. And, you know, we very definitely believe because of all of these reasons, right? It isn’t like people talk about this as sports science, or you talk about it as analytics. It’s not this is all about performance intelligence. And I feel like you know, 510 years ago, people talked about the video, we’re in the age of big data, the big data revolution is here, blah, blah, blah, blah. I think like we are very definitely in the age of performance intelligence now. And you know, the teams that are adopting this mindset and this philosophy, and basically, they’re not looking at this as a departmental piece or not saying we need a performance science department. They’re saying, we need to leverage intelligence across every aspect of what we do in the high performance sphere. That’s what good looks like. It’s not a departmental thing. It’s not something that you put on one person. It’s not about hiring a sports scientists. It is about organizational change and gearing your organization to leverage intelligence.
Alexander Ferguson 24:57
I’m coming from a very limited experience was Sports. Some of our viewers, I think we split up some rd know a lot more than I do about sports others, maybe my boat helped me understand, is it just de facto standard, every sports team now is digging into this data using it? Or are there any outliers that it’s actually not common yet?
Stephen Smith 25:16
Yeah, I think every organization is using data to some extent, is any organization maximizing potential of their data very well. Right? I think about that, again, like it’s quite a young industry, right? Think about, you know, where we were in 2007, with like, paper and pencil for like, you know, there was no wearable sensors, there was no optical trackers, there was no more RFID. So for an industry to, like, travel so far over the space of 15 years, like, it’s big, right? So, of course, there’s going to be a lag. Also, like, when you think about it, if you look at the companies that kind of evolved in this space, there’s a lot of like, small companies that have had 2025 30 people, for $5 million in funding, you know, there’s there hasn’t been like, you know, a big company has come in and said, Okay, we’re going to dominate this space, we’re going to own this space. And I think that’s why we’ve tried to grab that mantle and say, problems that we’re solving are super complex, right? So you know, that that requires a resource requires an incredible level of resource, and the challenges that we’re trying to solve, and the level of fidelity that we and our partners want to get to, is not simple. So that requires a huge investment. That’s why like, you know, we’ve raised close to $85 million over the last number of years. And it’s why we’re now you know, 135, like people, the company, we’re trying to add, we just next week to we’re trying to add another 112 people over the next 10 to 12 months. And it’s because, like, we believe, like, you know, they’re there, this industry deserves better, the technology has to improve, and the expectations of the industry have continued to rise, the emergence of more competent to the industry, and the ownership changes that we’ve seen in elite sport, there’s smart money, like and very modern business people coming into elite sport, and, and the that the couple that and their expectations, what they’ve seen in modern business, and then their expectations that of course, they would have in the sports business that they now own. And this massive explosion of data that we’ve had across the industry, and the stage is set, right, the stage is set. That’s why like, you know, going and really attacking that and somebody having the ambition and the drive and the courage to really say like, we’re going to go and do this, and we’re going to do it differently. And we’ll go we’re going to attack this and give people what we believe they need and like, raise the standard across this industry. Like, that’s what gets us excited every morning.
Alexander Ferguson 27:46
Do you fully believe it’s it’s this mass, this both both size of team, as well as the the backing of money behind it to really tackle this problem and requires this?
Stephen Smith 27:57
Oh, absolutely, like, the complexity of the issues, the people that we’re solving them for the level of their playing. It demands a level of quality, right? And, you know, I think you can’t get to that. You can’t tackle huge problems about resources, right? You need need a lot of people, you need a lot of capabilities, you need to be able to deploy the best technology, you need to be able to pay for that.
Alexander Ferguson 28:21
Are you seeing any pushback when when you’re sharing this concept and say, Hey, we’re spending a lot. We’re trying to tackle this, but people are saying, I don’t get it? I don’t care? Or is everyone saying yes, give it to me?
Stephen Smith 28:32
Yeah, to be honest, I think, you know, a number of years ago, we probably there probably was pushback in the industry. And there probably was people that were concerned, I’m thinking like, well, is this really gonna happen? I’m not sure. We’re just seeing these wearables come in, we’re seeing these optical trackers, but we’re not sure we’re going to use it. And people do not have a choice. The level of competitiveness within the industry has skyrocketed. TV revenues, sponsorship merchandise, the amount of money that has been spent on that right now. And who gets it. The people that are winning guys, right? People are winning the people that are performing at the highest level, the people that are picking up trophies are getting it. So if like, we’ve seen teams lean forward, get involved in this age of performance intelligence, and they’ve been making strides and they’ve been improving. And we’ve seen teams like, you know, pick up like trophies that you would never have when expected years ago because they’ve leaned into this announcement that others have to follow. And the reality is like, there is no choice. So this this happens to every industry, but they turned the data turned to analytics. It turned to intelligence that happens in every industry, and it changes industries. And we saw it wait like, you know, Blockbuster, and Netflix, right? If you ignore it, if you ignore this, you do so at your peril, and you can have an enormous business and you can be a huge organization. And overnight, you realize that you are five years behind, guess what, you don’t have five years anymore, and it’s over. And I think you know, I think the industry understands that I think there is a huge appetite for, you know, for people to do more. And the reality is the investment that’s already been made in technology today is huge. So why not we can maximize our investment? Why not maximize our data? Why not like turn that into intelligence do something powerful with.
Alexander Ferguson 30:15
For those that want to learn more, you can head over to Kitmanlabs.com. That’s kit man labs.com. Steven, thank you for for sharing your passion, your desire to change this whole ecosystem, this this industry and giving intelligence thanks for spending some time with us.
Stephen Smith 30:32
Yeah, no worries. Thank you so much for having me. I’ve really enjoyed this.
Alexander Ferguson 30:35
And we’ll see you all on the next episode of UpTech Report. Have you seen a company using AI machine learning or other technology to transform the way we live, work and do business? Go to UpTech report.com and let us know
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